US Regional Sales Analysis

GitHub Repo

Project Objective:

In this mini project I conducted a brief analysis of US Regional Sales data for 2019 as part of my Internship assignment at MeriSKILL using Microsoft Power BI.

• Cleaned & Transformed 186,000 sales records for 2019 across 19 products and 8 US states using Power Query.
• Reported insights like Key Products by Revenue (MacBooks & iPhones) and by Quantity sold (AAA Batteries), Top Cities by Revenue (San Francisco), Top States by Order values (Georgia) and peak Sales months (December & April).
• Tracked KPI Metric trends like Average Order value across US spatial data.
• Provided data-driven recommendations for Holiday Season Planning, Apple Product Promotions and boosting Customer Engagement in low sales months.

Analysis Insights:

Key findings from data trend analysis constitute insights into product preferences, sales trends, and regional variations. The Sales data revealed a significant spike in sales figures during December, which can be potentially attributed to the festive season.

Top Cities by Revenue:

• San Francisco (8.3M $) leads in revenue, possibly due to the in demand tech market and higher consumer spending power in the Silicon Valley.
• Los Angeles (5.5M $) and New York City (4.7M $) follow, likely owing to their large population density and economic activity.

Top States by Order Value:

• Georgia state (196.1 $) boasts the highest Average Order Value despite being the 5th state by Revenue and Total Orders.
• California state (192.1 $) lags behind as the 3rd lowest Average Order Value state despite being the highest state by Revenue and Total Orders.

Product Insights:

• Apple products, especially MacBooks (8M $) and iPhones (4.8M $) are the largest contributors to revenue, possibly due to high product demand and new product releases during year-end.
• AAA Battery (31K) and AA Battery (28K) while sold in high quantities, generate less revenue (93K $ and 106K $ respectively) indicating lower product prices.
• Thinkpad Laptop (4.1K) while sold in low quantity, generates the 3rd largest revenue (4.1M $) indicating higher product prices.

Sales by Month and Year:

• Sales peak in the month of December, aligning with the holiday season and gifting tradition.
• An increase in April might suggest spring-related sales or promotions.

Sales by Time:

• Peaks at 12 PM and 7 PM suggest lunchtime and after-work hours as prime buying times. Further analysis could explore targeted marketing during these hours for enhanced sales.

Data-driven Recommendations:

Based on the insights derived, it is recommended to capitalize on the festive season by strategizing marketing campaigns and product promotions. Additionally, efforts should be directed towards understanding consumer preferences to enhance product offerings and drive sales.

• Holiday Season Planning: Strategize targeted marketing campaigns and inventory management in the month of November for a expected surge in sales during the holiday season especially in December. Highlight popular high sales volume and revenue products and offer special discounts to boost sales.
• Apple Product Promotions: Align promotions and marketing efforts for Apple products, especially MacBooks and iPhones around December to maximize revenue during the year-end product release season.
• Product Diversification: Consider diversifying product offerings to include more high-margin items like the Thinkpad Laptop (4.1M $) while balancing revenue generation with high quantities and low-margin affordable products.
• Customer Engagement: Engage customers with loyalty programs or special offers on lower revenue generating months like January (1.8M $) and September (2.1M $) to boost sales.

Tools used:

• Microsoft Power BI Desktop: for Data Cleaning, Data Analysis, Visualization & Dashboard design
• Microsoft Power BI Service: for Publishing Report
• GitHub: for Project Documentation

Skills: Microsoft Power BI, Dashboards, Microsoft Power Query, DAX